The purchasing process involves an extended process of information gathering and decision-making by the buyer. In some cases, a variety of alternative product choices are available, differing in price and other characteristics. In other cases, the buyer configures a desired product by selecting among options for configurable elements of the product.
An effective salesperson begins the selling process with an information distribution phase in which the salesperson discloses general concepts relating to the product and specific product attributes. Based on this information, the customer selects a base product. The base product is either a single entity or an entity that includes a set of associated components. The salesperson helps the user customize the selected product by suggesting specific components to add to the base product and/or alterations to be made to components that may, by default, come with the base product. For each essential component associated with the base product, the salesperson helps the customer determine which component option is most appropriate for the needs of the customer. The salesperson also helps the customer evaluate optional components associated with the base product to determine if they should be purchased. Alternatively, in the case of a set of fixed product choices, the salesperson guides the buyer by incrementally changing a suggested choice based on systematically changing characteristics. In either case, the salesperson provides many types of important, timely, context sensitive information, suggestions and rationales to help the buyer make decisions. At times, the salesperson takes the initiative in leading the conversation while at other times the customer takes the initiative.
The approaches taken by the salesperson are adaptive. That is, throughout the sales process, the salesperson gauges customer characteristics, such as price sensitivity, interest in interacting with the salesperson, and experience level. The salesperson adjusts the sales pitch to match these characteristics. Thus, rather than pitching each of the available components associated with the base product in some predetermined inflexible way, the salesperson customizes his presentation to match the characteristics of the user.
Two types of electronic commerce software have been developed to aid users in the purchasing process. First, product selection software provides for the filtering of a set of products based on preferred product characteristics and desired uses. Second, configuration software allows a user to customize a product by selecting from lists of options and components provided by the software. Such configuration software is often used as a front-end for “build-to-order” manufacturing.
Typically, configuration software starts with a base system, selected by the user to satisfy a set of user goals as closely as possible. The configuration software provides a graphical user interface that presents a set of options that allow the user to customize each configurable element of the complete product. The configuration software often provides information about each option, usually as some form of specification sheet. Often, configuration software also provides information about each configurable element, such as the criteria for making a selection. Frequently asked questions may also be available. Configuration software often tracks constraints between choices of configurable elements. In some types of configuration software, option pairs that would violate such constraints are made unavailable. In other types of configuration software, the user is warned of any constraint violations as various options are selected. Deployments of configuration software, both as standalone applications and via networked systems, are available from Trilogy, Selectica, Calico Commerce, BT Squared and Siebel Systems.
Conventional electronic commerce configuration software focuses on the ease of selecting “correct” configurations, eliminating the need for human intervention by a salesperson or product expert. Much of the literature on configuration software emphasizes the savings provided by avoiding the “reworking” of inconsistent orders. But in eliminating the salesperson, such approaches also eliminate much of the personalized adaptive, heuristic behavior that make salespeople effective and helps to optimize both the shopping experience and final product choices.
Although existing electronic commerce sites utilize the above software tools to assist the purchaser, they do not provide methods comparable to that of a live salesperson. Some electronic commerce sites provide an electronic sales assistant that attempts to match the needs of the user to products on the electronic commerce site. Common product categories for which electronic sales assistants are currently used to select between pre-configured products include computers, automobiles, vacation destinations, pets, colleges and electronic devices. Sales assistance provided at sites that sell user configurable products, such as computers and automobiles, is far more limited.
But, as seen from such limited attempts at modeling the talents of a live salesperson, to date, no electronic commerce site has effectively reduced the personalized, heuristic, adaptive techniques of live salespeople to machine readable algorithms. For example, the deficiencies in the present use of electronic sales assistants to optimize the selection of a product from a set of pre-configured products is appreciated when the limitations of the features of representative implementations of conventional electronic sales assistants are examined. Representative electronic sales assistants include those offered by Ask Jeeves, Inc. on the etown.com site as “shop with Ida” and those offered by Silknet on the cozone.com site, as well as electronic sales assistants developed by America Online and found on aol.com and kaplan.com. These electronic sales assistants first elicit the goals of the user in terms of desired product features (such as manufacturer and weight), general usages (such as applications to be run), and budget. Existing electronic sales assistants provide either no recommendation or one or more recommendations based on either precise or approximate satisfaction of user goals. America Online ranks these recommendations in terms of how well they meet the goals of the user on a numeric scale while Silknet and Ask Jeeves cluster them within several categories of suitability. Also, the electronic sales assistants provide explanations, such as the ways in which a selected system meets the stated goals of the user (Ask Jeeves, America Online and Silknet).
The electronic sales assistants discussed above are unsatisfactory because they provide inadequate persistence within or across product optimization sessions. Thus, each iteration in the product optimization process is reduced to a “batch transaction,” in which the user mentally maintains comparisons and any sense of progress towards the purchase goal. This burden placed on the user greatly reduces the believability and effectiveness of such electronic sales assistants. Additionally, there is no attempt to match the buying style of the consumer, in terms of the style of interaction desired or the level and kind of information offered. Nor is there any attempt at inferring information about the purchaser based on behavior during the purchasing session or prior sessions. This lack of individualization results in a “one size fits all” assistant aimed at the middle of the targeted audience.
Prior art electronic sales assistants provide only limited help for selecting between pre-configured products and techniques used by these assistants are not adequate to optimize a build-to-order product. Because existing electronic commerce sites have failed to identify methods for integrating the features of a lives sales process into their sales algorithms, they have a significant disadvantage relative to competing physical commerce sites such as retail stores. Accordingly, what is needed in the art of electronic commerce and electronic sales assistants is a system and method for assisting customers in the optimization of products in a manner that exploits the adaptive and heuristic approach of the live sales process.